Embedding Space for Content Comprehension
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Solution Overview
Problem
Current content understanding systems lack the ability to differentiate question difficulty and relationships between questions, relying on neural networks to memorize answers without addressing question similarity or topic relevance.
Innovation Solution
Implementing a hierarchical knowledge taxonomy with question-answer pair adaptation, where a common embedding space is trained to embed related question and content vector representations closer together, allowing for the determination of relationships between questions of varying complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are used to memorize answers to questions, then the system can answer specific trained questions, but the system cannot answer similar questions or questions on similar topics where it was not specifically trained
Solution Approach 1:
The patent creates simplified question representations (question graphs) that copy the essential semantic structure of questions without requiring full neural network training. By extracting and representing key question elements in a standardized format, the system can handle similar questions through structural matching rather than memorization, reducing training complexity while maintaining answerability.
Solution Approach 2:
The system transforms questions into different parameter representations - converting natural language questions into structured question graphs with defined parameters and relationships. This parameter transformation allows the system to process and match questions based on their structural parameters rather than requiring identical training examples, thereby improving adaptability without proportional increases in training complexity.
2Ease of operation
If all questions are treated equally without differentiation, then the system can process any question uniformly, but the system cannot identify relationships between questions or determine their relative difficulty
Solution Approach 1:
The patent segments questions into distinct components and hierarchical levels within the question graph structure. By dividing questions into parameters, relationships, and nested sub-questions, the system can process each segment uniformly while simultaneously preserving the relationships between segments. This segmentation enables both ease of processing and retention of question relationships.
Solution Approach 2:
The system adds a structural dimension to question representation by creating question graphs with hierarchical levels and relationship types. This dimensional transformation allows the system to maintain uniform processing procedures while capturing question relationships and difficulty variations through the added structural dimension, preventing information loss.
3Measurement precision
If a hierarchical knowledge taxonomy is implemented to grade question complexity and relationships, then the system can identify relevant content and improve accuracy, but the system complexity increases
Solution Approach 1:
The hierarchical knowledge taxonomy is segmented into distinct levels and question graph components, allowing the system to apply complexity grading selectively to relevant elements rather than the entire system. This segmentation enables precise measurement of question and content relationships while keeping the overall system structure manageable through modular organization.
Data Source
AI summary
A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.